Landslide susceptibility maps (LSMs) rely on statistical association for weighting the impact of each predictive factor class. However, the effects of using different training datasets and causative factors combinations on the accuracy of the results are rarely discussed. Accordingly, we investigated the effects of training data and computation technique selection on the accuracy and performance of LSMs. Results show that including relict landslides in the training data diminishes the accuracy of the LSMs compared to models that exclude such processes from the input. As for the effect of choosing different combinations of predictive variables, it was shown to be less significant. After investigating association and causality between predictive variables, the best performing models produced using frequency ratio and logistic regression tend to be more accurate than those including all variables. As for the artificial neural networks, it seems to outperform other models. Indeed, such investigations are beneficial for improving LSMs.

The geomorphological control on landslides susceptibility maps (LSM) and frequency-area distributions (FAD) is under-investigated. Hence, we investigate LSMs and FAD distributions in two contrasted landscapes of Taounate province using explanatory statistical techniques. In the geologically heterogenous context, our findings show that LSM spatial distributions for deep landslides are different tot those of shallow processes, regardless of the LSM technique used. The Predictive factors also differ between both types of mass movements in this rugged and heterogenous zone. However, the opposite is observed in the geologically homogenous South, where shallow and deep landslides seem to be controlled by the same factors and tend to produce LSMs that are similarly distributed across the study area. This is further emphasized by the Area Under Curve (AUC) results which yield good to very good values in the South regardless of the types of landslides used for validation, with average to bad values in the North when shallow landslides are used to validate deep LSMs and vice versa. The same can be said about FADs which are also sensitive to landscape variations.